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MongoDB - Map Reduce


In this chapter we discuss MongoDB - Map Reduce

Map-lessen is a statistics processing paradigm for condensing huge volumes of records into beneficial aggregated consequences.

MongoDB uses mapReduce command for map-reduce operations. MapReduce is usually used for processing massive statistics units.

MapReduce Command

>db.Collection.MapReduce(
   characteristic() emit(key,cost);,  //map feature
   characteristic(key,values) return reduceFunction,    //lessen characteristic
      out: collection,
      query: record,
      type: file,
      restriction: wide variety
   
)
There are following syntax -
  • map is a javascript characteristic that maps a fee with a key and emits a key-cost pair

  • reduce is a javascript feature that reduces or agencies all of the files having the identical key

  • out specifies the vicinity of the map-reduce query result

  • query specifies the non-obligatory selection criteria for choosing documents

  • type specifies the elective kind standards

  • limit specifies the optionally available maximum quantity of documents to be back

Using MapReduce

Consider the subsequent report structure storing person posts. The file shops user_name of the person and the repute of publish.

   "post_text": "Angeline Jolie is splendid Hollywood actress ",
   "user_name": "angle",
   "fame":"active"


we are able to use a mapReduce characteristic on our posts collection to pick all of the energetic posts, organization them on the basis of user_name and then be counted the number of posts through every consumer the use of the following code
>db.Posts.MapReduce( 
   function()  emit(this.User_id,1); , 
	
   feature(key, values) go back Array.Sum(values),   
      query:reputation:"active",  
      out:"post_total" 
   
)

The above mapReduce query outputs the subsequent result
   "result" : "post_total",
   "timeMillis" : 9,
   "counts" : 
      "enter" : four,
      "emit" : 4,
      "reduce" : 2,
      "output" : 2
   ,
   "ok" : 1,

The result suggests that a complete of 4 files matched the query (popularity:"energetic"), the map characteristic emitted 4 documents with key-value pairs and in the end the lessen characteristic grouped mapped documents having the same keys into 2.

To see the result of this mapReduce query, use the discover operator
>db.Posts.MapReduce( 
   function()  emit(this.User_id,1); , 
   function(key, values) go back Array.Sum(values),   
      question:fame:"energetic",  
      out:"post_total" 
   
	
).Discover()
The above query gives the following end result which shows that both customers Peterson and Dewayne have two posts in energetic states
 "_id" : "Peterson", "value" : 2 
 "_id" : "Dewayne", "fee" : 2 
a comparable manner, MapReduce queries may be used to assemble big complex aggregation queries. The use of custom Javascript capabilities employ MapReduce which could be very bendy and powerful.



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